System and method for efficiently transmitting signals with repeating structures

ABSTRACT

A system and method for efficiently transmitting signals from a sensing device to a remote device are disclosed. The sensing device includes at least one sensor for sensing information. The sensed information could be associated with heart activity. The sensing device smooths the sensed signal and then calculates the difference between the smoothed sensed signal and a locally stored baseline signal to determine a differential signal, which can be transmitted as a smaller packet of data than the full sensed signal. The remote device receives information associated with the differential signal and uses a local copy of the baseline signal to reconstruct the original smoothed sensed signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Provisional Patent ApplicationNo. 62/880,709 filed Mar. 31, 2019, and titled “System and Method forEfficiently Transmitting Signals with Repeating Structures,” which isincorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to data transmission, and inparticular to a system and method for transmitting data efficiently.

BACKGROUND

Many wearable devices now exist that can detect biometric data,including data such as heartbeats, steps taken, perspiration levels, aswell as other kinds of information. These devices are often small andhave limited computing resources to analyze and apply the sensed data.Analysis and long-term storage of the data is usually accomplished by aserver.

Users of these devices may find themselves in locations with limitedconnectivity. Additionally, the devices may have low-power requirements.When the sensed data is real-time, or otherwise nearly continuous, itmay be especially difficult to transmit all the data to a server forfurther processing and storage.

There is a need in the art for a system and method that addresses theshortcomings discussed above.

SUMMARY

In one aspect, a method of improving the efficiency of transmittingsensed information from a sensing device to a receiving device includessteps of sensing a signal with a sensor, retrieving a baseline signal,transforming the sensed signal into a smoothed sensed signal,determining differences between the smoothed sensed signal and thebaseline signal, and sending at least some of the differences betweenthe smoothed sensed signal and the baseline signal to the receivingdevice.

In another aspect, a method of reconstructing a sensed signal receivedfrom a sensing device includes a step of receiving data, where the datacomprising differences between a sensed signal and a first baselinesignal. The method also includes a step of retrieving a second baselinesignal, where the second baseline signal is substantially similar to thefirst baseline signal. The method also includes a step of reconstructinga new signal from the baseline signal and the data.

In another aspect, a communication system for efficiently transmittingsignals includes a sensing device. The sensing device further includes asensor, a first baseline signal stored in a first memory, and a signalprocessing unit. The communication system also includes a remote devicethat further includes a second baseline signal stored in a second memoryand a signal processing unit. The second baseline signal issubstantially identical to the first baseline signal. The sensing deviceis configured to detect a sensed signal using the sensor, transform thesensed signal into a smoothed sensed signal, determine differencesbetween the smoothed sensed signal and the first baseline signal, andtransmit the differences between the smoothed sensed signal and thefirst baseline signal to the analyzing system. The remote device isconfigured to receive the differences between the smoothed sensed signaland the first baseline signal transmitted by the sensing device, and usethe second baseline signal and the differences between the smoothedsensed signal and the first baseline signal to reconstruct the smoothedsensed signal.

Other systems, methods, features, and advantages of the disclosure willbe, or will become, apparent to one of ordinary skill in the art uponexamination of the following figures and detailed description. It isintended that all such additional systems, methods, features, andadvantages be included within this description and this summary, bewithin the scope of the disclosure, and be protected by the followingclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be better understood with reference to the followingdrawings and description. The components in the figures are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention. Moreover, in the figures, likereference numerals designate corresponding parts throughout thedifferent views.

FIG. 1 is a schematic view of a user deployed in a disaster area whoseheart rate is being monitored at a separate location, according to anembodiment;

FIG. 2 is a schematic view of a communication system, according to anembodiment;

FIG. 3 is a schematic view of a process associated with thecommunication system of FIG. 2, according to an embodiment;

FIG. 4 is a schematic view of a process for determining a user specificbaseline, according to an embodiment;

FIG. 5 is a schematic view of a step of smoothing a sensed signal,according to an embodiment;

FIG. 6 is a schematic view of a step of determining the differencebetween a smoothed sensed signal and a baseline signal;

FIG. 7 is a schematic view of a step of reconstructing a smoothed sensedsignal from a baseline signal and transmitted data;

FIG. 8 is a schematic view of another process associated with acommunication system, according to an embodiment; and

FIG. 9 is a schematic view of another process associated with acommunication system, according to an embodiment.

DESCRIPTION OF THE EMBODIMENTS

A system and method for efficiently transmitting signals from a sensingdevice to a remote device are disclosed, which overcome the limitationsaddressed above. Specifically, the system and method allow signals withregular or repeated structure to be sent in an efficient manner. Thatis, the amount of data that must be transmitted between two devices issignificantly less than the amount of data required to encode theoriginal signal. Moreover, this process differs from other source codingmethods by leveraging repetition/patterns in the signal (encoded in abaseline signal) that are known a priori. With knowledge of the typical,or average, behavior of the signal, only differences between the sensedsignal and a baseline signal must be sent. Upon receiving thedifferences in the signals, a receiving device may reconstruct thesensed signal using the transmitted differences along with a local copyof the baseline signal. Not only is the amount of data transferredreduced because much of the signal is encoded in the baseline signalthat already exists at the sending and receiving devices, but thepresent system and method also help minimize power consumption by thetransmitting device (the sensing device). For example, if a real-timecontinuous signal needs to be transmitted from a sensing device to areceiving device, the sensing device can transmit only at times when thesignal differs substantially from the baseline signal.

FIG. 1 is a schematic view of an exemplary situation where signals mayneed to be transmitted from a device in one location to a server orother system at a second location. In the example shown in FIG. 1, auser 100 is an emergency responder who has been deployed to a disasterarea 105. That is, an area that has recently experienced a flood, fire,hurricane, tornado, or other disaster. In this case, disaster area 105is an area experiencing wild fires. User 100 has a wearable device 102that has sensors that are able to continuously monitor an electricalsignal associated with the user's heart function. This sensed electricalsignal 110 can then be sent to a remote server 120 where it can bemonitored for signs of stress that might indicate that user 100 is introuble and needs to be evacuated or otherwise provided with additionalsupport.

In disasters, communication infrastructure (such as cell towers) may bedamaged which leads to limited connectivity between user 100 and remoteserver 120. Because of both this limited connectivity, and the powerconstraints of many wearable devices (such as device 102), any signalstransmitted between device 102 and server 120 should be as efficient aspossible. That is, the amount of data transmitted should be as small aspossible due to possible latency issues on the network. Also, the amountof time the device spends transmitting should be limited to helpconserve power.

FIG. 2 is a schematic view of a communication system 200 thatfacilitates efficient communication for some types of signals.Specifically, system 200 facilitates efficient communication for signalsthat have predictable baselines, or predictable repeated structure. Oneexample of an electrical signal with predictable repeated structureincludes electrical signals associated with heart function, such as thesensed electrical signal 110 in FIG. 1. This type of signal can begenerated by placing one or more electrodes on a person's skin. If thesignal is generated using a sufficient number of electrodes inparticular places, the plot of the signal is often referred to as anelectrocardiogram (ECG). These signals may be decomposed into normalpatterns of heart activity. For example, the portion of a signalassociated with each heart beat is understood to include a P wave, a QRScomplex, a T wave and a U wave. Based on patterns of these entities,heart rate and physiological heart rhythms can be identified andmonitored for anomalies such as bradycardia (below normal heart rate),tachycardia (above normal heart rate), and cardiac arrhythmias(irregular rhythms). Communication system 200 could also be used totransmit other sensed signals associated with a user and/or the user'senvironment. For example, continuously monitored temperature,perspiration, breathing, and other vital indicators or behaviors couldbe used provided the sensing device is equipped with suitable sensors todetect the associated signals.

In other embodiments, communication system 200 could be used toefficiently transmit other kinds of sensed signals with regular orrepeating baseline patterns. For example, after an earthquake, signalsfrom one or more seismometers could be transmitted from sensing devicesin the earthquake area to a remote device over a network.

Communication system 200 includes a sensing device 202 and a remotedevice 204 that is disposed at a different location from sensing device202. In particular, it may be assumed that remote device 204 issufficiently far enough from sensing device 202 that signaltransmissions can be degraded by intermediate communicationinfrastructure. Sensing device 202 could include any device capable ofsensing information. In some embodiments, sensing device 202 could be amobile device, such as a mobile phone or tablet. In other embodiments,sensing device 202 could be a wearable device, such as a smartwatch orfitness tracker. Remote device 204 could be any device with sufficientcomputing resources to receive data from sensing device 202. In somecases, remote device 204 could be a server. In the exemplary embodiment,remote device 204 also includes provisions for storing andmonitoring/analyzing the received signals. However, in otherembodiments, a remote device could simply be a receiving device andcould pass received signals to yet other systems or devices for analysisand/or storage.

Sensing device 202 may include a processor 220 and memory 222. Memory222 may comprise a non-transitory computer readable medium. Instructionsstored within memory 222 may be executed by the one or more processors220.

Sensing device 202 may also include one or more sensors 240. The type ofsensors used may vary from one embodiment to another and may depend onthe type of sensing device. In an embodiment where sensing device 202 isa smartwatch or other wearable, sensors 240 could include heart ratemonitors (such as an optical blood flow sensor 241 that can be used toinfer heart rate), an oximetry sensor 242 to measure blood oxygen, and arespiration sensor 243. In some cases, a respiration sensor could be aradar-based sensor capable of detecting very small movements, such asthe rise and fall of a user's chest. Other sensors could include a skinconductance sensor and a skin temperature sensor. Still other sensorscould include sensors found in many mobile devices, such as a gyroscope,an accelerometer, a GPS receiver, as well as other suitable sensors.

Sensing device 202 may further include a signal processing unit 250.Signal processing unit 250 includes provisions for processinginformation retrieved from the one or more sensors 240. Specifically,signal processing unit 250 includes a smoothing module 252, a differencemodule 254 and one or more baseline signals 256 that may be stored inmemory. As described in further detail below, signal management unit 250may transform an incoming signal from sensors 240 using these modulesand baseline signals.

Smoothing module 252 may include algorithms for smoothing a sensedsignal, which may contain a lot of noise. Smoothing module 252 couldimplement a variety of different smooth algorithms. In some cases,smoothing module 252 could implement an unweighted sliding-averagesmoothing algorithm. In other cases, smoothing module 252 couldimplement the Savitzky-Golay algorithm, which is another well-knownsmoothing algorithm based on least-squares fitting.

Difference module 254 may include algorithms for calculating thedifference between two signals. Any known algorithms for computing thedifferences between two signals could be implemented by differencemodule 254. If the signals have a time lag, techniques such ascross-correlation could be used to determine the time lag so that thedifference can be computed. Additionally, difference module 254 couldimplement other provisions to normalize the signals or otherwise adjustthe signals in a way that makes a direct subtraction or other comparisonmore suitable.

Baseline signals 256 may comprise one or more predicted or expectedsignals. These signals may be specific to a particular context. Forexample, as described in further detail below, electrical signals ofheartbeat activity may be associated with an expected or predictedbaseline signal for normal heart function.

Remote device 204 may include a processor 260 and memory 262. Memory 262may comprise a non-transitory computer readable medium. Instructionsstored within memory 262 may be executed by the one or more processors260.

Remote device 204 may also include an analysis module 266. Analysismodule 266 may include one or more software applications or algorithmsfor analyzing signals received from sensing device 202. The type ofanalysis used may depend on the context. In an embodiment where anelectrical signal associated with heart activity is received, theanalysis module could comprise a tool for monitoring the heart activity,detecting abnormal heart activity, and providing notifications whenabnormal activity is detected.

Remote device 204 also includes a signal processing unit 270. Signalprocessing unit 270 includes provisions for processing received signals.Specifically, signal processing unit 270 includes a reconstructionmodule 272 and one or more baseline signals 274 that can be used toreconstruct a signal, as described in further detail below.

In some cases, remote device 204 also communicates with a separatedatabase 280. Database 280 may be used to store baseline signals,software applications or other data. Additionally, signals processed byremote device 204 may also be stored in database 280.

Sensing device 202 may communicate with remote device 204 over a network210. Network 210 could be any wide area network (WAN), local areanetwork (LAN), and/or personal area network (PAN). In some embodiments,network 210 could comprise a cellular network that connects sensingdevice 202 and remote device 204 over the internet.

To facilitate communication, each of sensing device 202 and remotedevice 204 may include a communication system. Specifically, sensingdevice 202 includes a first communication system 290 and remote device204 includes a second communication system 292. Each communicationsystem may include radios or other provisions for communicating usingone or more communication methods. For example, each communicationsystem could include a Wi-Fi radio, a Bluetooth radio, and/or a cellularnetwork radio.

In some embodiments, two or more sensing devices could communicatedirectly with one another over a mesh network. In some cases, the meshnetwork could be a Bluetooth based mesh network. In such cases, datacould be transferred between the devices over the mesh network so thatdevices with sufficient connectivity to remote device 204 over a widearea network could transmit and receive data on behalf of one or more ofthe sensing devices. Such a configuration may allow devices withinsufficient connectivity to transmit signal information to otherdevices that have sufficient connectivity to ensure the data can bereceived at remote device 204.

FIG. 3 is a schematic view of a process of sending a signal in anefficient manner between a sensing device and a remote device. As shownin FIG. 3, some of the steps may be performed by sensing device 202,while other steps may be performed by remote device 204.

Starting in step 302, sensing device 202 may receive a sensed signal.Specifically, signal processing unit 250 may receive information fromone or more of sensors 240. For example, if device 202 is a smart watchwith a sensor capable of detecting heart activity, signal processingunit 250 may receive information corresponding to a user's heartactivity.

Next, in step 304, sensing device 202 may retrieve a baseline signal. Ifthe sensed signal is associated with heart activity, the baseline signalmay be a baseline heart activity signal. In some embodiments, theretrieved baseline signal may be an average baseline signal for a givenpopulation. In other embodiments, the retrieved baseline signal could bespecific to a particular user. For example, the process shown in FIG. 4depicts how a user specific baseline can be determined. In particular,in a first step 402, a system can receive a measured signal from asensor for a predetermined period of time. The period of time may dependon the context. For heart rate activity, a baseline could be establishedby monitoring heart activity over a few minutes or hours. Next, in step404, a system may determine a user specific baseline signal according tothe measured signal. To do this, the system may identify repeatingpatterns in the data and determine an average signal over time (or overa given interval). Finally, in step 406, the system may store the userspecific baseline signal for later use. For example, the user specificbaseline signal may be stored on both a sensing device (such as sensingdevice 202) and a remote device (such as remote device 204).

Referring back to the process in FIG. 3, once the baseline signal hasbeen retrieved, sensing device 202 may transform the raw sensed signalto a smoothed sensed signal in step 306. Specifically, the signal can besmoothed using smoothing module 252 to remove noise and/or variations inthe signal below a particular threshold. FIG. 5 illustrates a schematicview comparing a pre-processed (that is, raw) signal 502 and a smoothedsignal 504. The global shape of these signals is substantially similar,however raw signal 502 includes significantly more noise in the signalas shown in the comparison between enlarged segment 510 of raw signal502 and enlarged segment 512 of smoothed signal 504.

Next, in step 308, sensing device 204 determines the differences betweenthe retrieved baseline signal and the raw sensed signal. This processcan be seen schematically in FIG. 6. Here, a baseline signal 602 issubtracted from smoothed signal 504 to produce a differential signal610. Differential signal 610 has a value of 0 at all locations except ananomalous segment 612. This anomalous segment 612 corresponds to asegment 520 of smoothed signal 504 where the heart activity fluctuatessubstantially from the baseline, or expected, activity. Such afluctuation could indicate an abnormal heart beat, for example.

In step 310, sensing device 202 sends only the differences between thesmoothed sensed signal and the baseline signal to remote device 204.Because the values of the differential signal are substantially equal to0 at all but a small number of locations, this results in asignificantly more efficient transmission of data than if the entiresensed signal (or smoothed sensed signal) were transmitted. Inparticular, sensing device 302 need not send data for the portions ofthe signal that are substantially close to 0. Instead, as indicated inFIG. 6, sensing device 202 may only send data associated with theanomalous segment 612. With this configuration, the transmission timecan be substantially reduced compared to transmitting the full signal.This is because sensing device 202 need only transmit data during briefintervals when the signal differs substantially from the baselinesignal. This has the effect of reducing the power consumed by using oneor more radios of sensing device 202.

In some embodiments, difference data may only be sent when the valuesare outside a particular threshold range. In some cases, the absolutevalue of the threshold range may be determined according to the valuesof the raw signals and/or baseline signals. As an example, if the rawand baseline signals are normalized to have maximum values of 1, thethreshold range could be selected to be −0.1 to 0.1 (that is, plus orminus 10 percent of the maximum value). In other words, if the absolutevalue of the difference between the raw (or smoothed) signal and thebaseline signal is less than 0.1, the algorithm may treat them as beingsubstantially 0 and the values need not be transmitted. Of course, theseparticular values are only intended to be illustrative and the thresholdvalues used in other embodiments may be selected to balance datatransmission efficiency with accuracy of the reconstructed signal.

In step 312, remote device 204 receives the differences in the twosignals (that is, the nonzero segments of the differential signal).Next, remote device 204 retrieves a local copy of the baseline signal instep 314. This baseline signal may be identical to the baseline signalstored on sensing device 202 and used to determine the differentialsignal.

In step 316, remote device 204 uses the baseline signal and the receiveddifferential signal to reconstruct the smoothed sensed signal that wasinitially created by sensing device 202. This process can be seenschematically in FIG. 7. In this case, the baseline signal 602 (storedlocally) is added to the differential signal 610 (received from thesensing device) to reconstruct the smoothed sensed signal 504.

After reconstructing the smoothed sensed signal 504, remote device 204may analyze and/or store the reconstructed signal in step 318. Forexample, in the exemplary situation shown in FIG. 1, remote device 204could be used to monitor the heart activity of user 100 and providealerts to another party if any anomalies have been detected that wouldindicate that user 100 is stressed or should otherwise be evacuated fromthe disaster area.

In embodiments where a sensing device may be capable of detecting one ormore different kinds of signals, the sensing device could be configuredto automatically determine an appropriate baseline to use with eachdifferent signal. For example, if the sensing device receives heartactivity information, it may automatically select a baseline heartactivity signal. If, however, the device receives a signal correspondingto breathing activity information, it may automatically select abaseline breathing activity signal.

FIG. 8 is an exemplary process 803 where a sensing device 800automatically chooses an appropriate signal category and correspondingbaseline signal. Starting in step 802, sensing device 202 may receive asensed signal from one or more sensors. Next, in step 804, sensingdevice 202 may categorize (or classify) the signal. Categorization orclassification could be accomplished using a machine learning algorithmto distinguish between different patterns of classes. Alternatively,algorithms could be used that search the signals for distinctive“signatures” or patterns that are unique to that particular kind ofsignal.

Once the signal has been categorized, an appropriate baseline signal maybe retrieved in step 806. Following this, sensing device 202 maytransform the sensed signal to a smoothed signal in step 808. Next,sensing device 202 may determine the differences between the smoothedsensed signal and the baseline signal in step 810. These differences maybe sent to a remote device 801 in step 812. In addition, to sending thedifferences, sensing device 202 may also send the signal category thatwas determined in step 804.

Remote device 801 receives the differences between the smoothed sensedsignal and the baseline signal along with the signal category in step814. In step 816, remote device 801 retrieves the appropriate baselinesignal (from multiple) according to the signal category. In step 818,remote device 801 reconstructs the smoothed sensed signal from thebaseline signal and the differences received in step 814.

In step 820, remote device 801 may monitor the reconstructed signal. Ifan anomaly is detected in step 822, remote device 801 may send an alertor notification to a user in step 824. Here, the user could be anoperator of the remote device, the person wearing the sensing device, orany other party. If no anomalies are detected in step 822, remove device801 may return to step 818 to continue monitoring the signal.

Some sensing devices may include provisions for transmitting signalinformation based on the type of sensed information and its priorityrelative to other types of sensed information. For example, a sensingdevice may be configured to prioritize heart rate information overrespiration information if the heart rate sensors are deemed moreaccurate than the respiration sensors. In that case, the sensing devicemay not only prioritize transmitting differences between the heart ratesignal and the baseline heart rate signal over sending otherinformation, but the sensing device may also store the full heart ratesignal and attempt to transmit the full signal when connectivity issufficient.

FIG. 9 is a schematic view of a process for sending signal informationaccording to priority levels and rules associated with the prioritylevels. Starting in step 902, a sensing device may retrieve signalpriorities and associated rules for each type of signal. As an example,signal priorities could comprise three priority levels or types: a highpriority, a moderate priority and a low priority. Alternatively, in someembodiments, each signal could be ranked so that the prioritycorresponds to the ranking. Next, in step 904, the sensing device mayreceive a sensed signal from a sensor (or multiple sensors). In step906, the sensing device could determine the differences between thesensed signal and a baseline signal, as already described above. In step908, the differences between the sensed signal and baseline signal couldbe sent to a remote device, or to other devices in a mesh network.

In step 910, the sensing device determines the priority of the sensedsignal. If the sensed signal has a high priority, then the sensingdevice proceeds to step 912. In step 912, the sensing device may savethe full signal (either the raw signal or the smoothed signal, forexample). The sensing device may then wait to transmit the full signalwhen there is sufficient connectivity to transmit this higher quantityof data.

If the sensed signal has a moderate priority as determined in step 910,the sensing device proceeds to step 914. In step 914, the sensing devicemay save the full signal only if there is storage available.Specifically, in some cases, a smaller amount of storage may beallocated to moderate priority signals compared to high prioritysignals. The sensing device may then wait to transmit the full signalaccording to priority. For example, the sensing device may not transmitthe full signal until all high priority signals have already beentransmitted.

If the sensed signal has a low priority, as determined in step 910, thesensing device proceeds to step 916. In step 916, the sensing device maynot save the full signal. Also, in some cases, the sensing device maynot send the full signal. That is, for low priority signals, only thedifferences may be transmitted.

The processes and methods of the embodiments described in this detaileddescription and shown in the figures can be implemented using any kindof computing system having one or more central processing units (CPUs)and/or graphics processing units (GPUs). The processes and methods ofthe embodiments could also be implemented using special purposecircuitry such as an application specific integrated circuit (ASIC). Theprocesses and methods of the embodiments may also be implemented oncomputing systems including read only memory (ROM) and/or random accessmemory (RAM), which may be connected to one or more processing units.Examples of computing systems and devices include, but are not limitedto: servers, cellular phones, smart phones, tablet computers, notebookcomputers, e-book readers, laptop or desktop computers, all-in-onecomputers, as well as various kinds of digital media players.

The processes and methods of the embodiments can be stored asinstructions and/or data on non-transitory computer-readable media. Thenon-transitory computer readable medium may include any suitablecomputer readable medium, such as a memory, such as RAM, ROM, flashmemory, or any other type of memory known in the art. In someembodiments, the non-transitory computer readable medium may include,for example, an electronic storage device, a magnetic storage device, anoptical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of suchdevices. More specific examples of the non-transitory computer readablemedium may include a portable computer diskette, a floppy disk, a harddisk, magnetic disks or tapes, a read-only memory (ROM), a random accessmemory (RAM), a static random access memory (SRAM), a portable compactdisc read-only memory (CD-ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), electrically erasable programmableread-only memories (EEPROM), a digital versatile disk (DVD and DVD-ROM),a memory stick, other kinds of solid state drives, and any suitablecombination of these exemplary media. A non-transitory computer readablemedium, as used herein, is not to be construed as being transitorysignals, such as radio waves or other freely propagating electromagneticwaves, electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Instructions stored on the non-transitory computer readable medium forcarrying out operations of the present invention may beinstruction-set-architecture (ISA) instructions, assembler instructions,machine instructions, machine dependent instructions, microcode,firmware instructions, configuration data for integrated circuitry,state-setting data, or source code or object code written in any of oneor more programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or suitable language, and proceduralprogramming languages, such as the “C” programming language or similarprogramming languages.

Aspects of the present disclosure are described in association withfigures illustrating flowcharts and/or block diagrams of methods,apparatus (systems), and computing products. It will be understood thateach block of the flowcharts and/or block diagrams can be implemented bycomputer readable instructions. The flowcharts and block diagrams in thefigures illustrate the architecture, functionality, and operation ofpossible implementations of various disclosed embodiments. Accordingly,each block in the flowchart or block diagrams may represent a module,segment, or portion of instructions. In some implementations, thefunctions set forth in the figures and claims may occur in analternative order than listed and/or illustrated.

The embodiments may utilize any kind of network for communicationbetween separate computing systems. A network can comprise anycombination of local area networks (LANs) and/or wide area networks(WANs), using both wired and wireless communication systems. A networkmay use various known communications technologies and/or protocols.Communication technologies can include, but are not limited to:Ethernet, 802.11, worldwide interoperability for microwave access(WiMAX), mobile broadband (such as CDMA, and LTE), digital subscriberline (DSL), cable internet access, satellite broadband, wireless ISP,fiber optic internet, as well as other wired and wireless technologies.Networking protocols used on a network may include transmission controlprotocol/Internet protocol (TCP/IP), multiprotocol label switching(MPLS), User Datagram Protocol (UDP), hypertext transport protocol(HTTP), hypertext transport protocol secure (HTTPS) and file transferprotocol (FTP) as well as other protocols.

Data exchanged over a network may be represented using technologiesand/or formats including hypertext markup language (HTML), extensiblemarkup language (XML), Atom, JavaScript Object Notation (JSON), YAML, aswell as other data exchange formats. In addition, informationtransferred over a network can be encrypted using conventionalencryption technologies such as secure sockets layer (SSL), transportlayer security (TLS), and Internet Protocol security (Ipsec).

While various embodiments of the invention have been described, thedescription is intended to be exemplary, rather than limiting, and itwill be apparent to those of ordinary skill in the art that many moreembodiments and implementations are possible that are within the scopeof the invention. Accordingly, the invention is not to be restrictedexcept in light of the attached claims and their equivalents. Also,various modifications and changes may be made within the scope of theattached claims.

We claim:
 1. A method of improving the efficiency of transmitting sensedinformation from a sensing device to a receiving device, comprising:sensing a first sensed signal with a first sensor of the sensing device;applying machine learning to the first sensed signal to classify thefirst sensed signal as belonging to a first signal category of aplurality of signal categories, wherein each signal category in theplurality of signal categories has a different signal pattern;retrieving a first baseline signal corresponding to the first signalcategory; transforming the first sensed signal into a first smoothedsensed signal; determining differences between the first smoothed sensedsignal and the first baseline signal; sending at least some of thedifferences between the first smoothed sensed signal and the firstbaseline signal to the receiving device; sensing a second sensed signalwith a second sensor of the sensing device, wherein the second sensedsignal is different from the first sensed signal; applying machinelearning to the second sensed signal to classify the second sensedsignal as belonging to a second signal category of the plurality ofsignal categories; retrieving a second baseline signal corresponding tothe second signal category, wherein the second baseline signal isdifferent than the first baseline signal; transforming the second sensedsignal into a second smoothed sensed signal; determining differencesbetween the second smoothed sensed signal and the second baselinesignal; sending at least some of the differences between the secondsmoothed sensed signal and the second baseline signal to the receivingdevice; determining a first signal priority for the first sensed signaland determining a second signal priority for the second sensed signal;and wherein after sending the at least some of the differences betweenthe first smoothed sensed signal and the first baseline signal andsending the at least some of the differences between the second smoothedsensed signal and the second baseline signal, the method furthercomprises: sending the first sensed signal to the remote device based onthe first signal priority of the first sensed signal.
 2. The methodaccording to claim 1, wherein the method further comprises sending thesecond sensed signal to the remote device based on the second signalpriority of the second sensed signal.
 3. The method according to claim2, wherein the order in which the first sensed signal and the secondsensed signal are transmitted is determined by the first signal priorityand the second signal priority.
 4. The method according to claim 1,wherein the method further comprises not sending the second sensedsignal to the remote device based on the second signal priority of thesecond sensed signal.
 5. The method according to claim 1, whereindetermining the differences between the first smoothed sensed signal andthe first baseline signal includes using cross-correlation to determinelag between the first sensed signal and the first baseline signal. 6.The method according to claim 1, wherein sending at least some of thedifferences between the first smoothed sensed signal and the firstbaseline signal includes transmitting only differences that aresubstantially greater than zero.
 7. The method according to claim 1,wherein the first baseline signal is stored prior to sensing the firstsensed signal with the first sensor.
 8. The method according to claim 7,wherein the method further includes sending the first signal category tothe receiving device.
 9. The method according to claim 1, the methodfurther comprising: receiving first data, the first data comprisingdifferences between the first sensed signal and the first baselinesignal; receiving the first signal category associated with the firstdata; retrieving the first baseline signal according to the receivedfirst signal category, reconstructing a first new signal from the firstbaseline signal and the first data; receiving second data, the seconddata comprising differences between the second sensed signal and thesecond baseline signal; receiving the second signal category associatedwith the second data, wherein the second signal category is differentthan the first signal category; retrieving the second baseline signalaccording to the received second signal category; reconstructing asecond new signal from the second baseline signal and the second data;and after reconstructing the first baseline signal and the secondbaseline signal, receiving the first sensed signal.
 10. The methodaccording to claim 9, wherein the first sensed signal has a higherpriority than the second sensed signal.
 11. The method according toclaim 9, wherein after receiving the first sensed signal the methodfurther comprises receiving the second sensed signal.
 12. The methodaccording to claim 11, wherein the method further comprises monitoringthe first new signal and detecting anomalous heart activity in the firstnew signal.
 13. The method according to claim 12, wherein the methodfurther comprises sending an alert to a user when the anomalous heartactivity is detected.
 14. A communication system for efficientlytransmitting signals, comprising: a device processor; a non-transitorycomputer readable medium storing instructions that are executable by thedevice processor to: sense a first sensed signal with a first sensor;apply machine learning to the first sensed signal to classify the firstsensed signal as belonging to a first signal category of a plurality ofsignal categories, wherein each signal category in the plurality ofsignal categories has a different signal pattern; retrieve a firstbaseline signal corresponding to the first signal category; transformingthe first sensed signal into a first smoothed sensed signal; determiningdifferences between the first smoothed sensed signal and the firstbaseline signal; send at least some of the differences between the firstsmoothed sensed signal and the first baseline signal to the receivingdevice; sense a second sensed signal with a second sensor, wherein thesecond sensed signal is different from the first sensed signal; applymachine learning to the second sensed signal to classify the secondsensed signal as belonging to a second signal category of the pluralityof signal categories; retrieve a second baseline signal corresponding tothe second signal category, wherein the second baseline signal isdifferent than the first baseline signal; transform the second sensedsignal into a second smoothed sensed signal; determine differencesbetween the second smoothed sensed signal and the second baselinesignal; send at least some of the differences between the secondsmoothed sensed signal and the second baseline signal to the receivingdevice; determine a first signal priority for the first sensed signaland determine a second signal priority for the second sensed signal; andsend the first sensed signal to the remote device based on the firstsignal priority of the first sensed signal after sending the at leastsome of the differences between the first smoothed sensed signal and thefirst baseline signal and sending the at least some of the differencesbetween the second smoothed sensed signal and the second baselinesignal.
 15. The communication system according to claim 14, wherein theinstructions are executable to send the second sensed signal to theremote device based on the second signal priority of the second sensedsignal.
 16. The communication system according to claim 15, wherein theorder in which the first sensed signal and the second sensed signal aretransmitted is determined by the first signal priority and the secondsignal priority.
 17. The communication system according to claim 14,wherein the first sensor is an optical blood flow sensor.
 18. Thecommunication system according to claim 14, wherein the first sensor isan oximetry sensor.
 19. The communication system according to claim 14,wherein the first sensor is a respiration sensor.
 20. The communicationsystem according to claim 14, wherein the first sensed signal is anelectrical signal associated with heart activity.